Demon-assist adiabatic computing numerical data

In [23]:

    

using QuDynamics
using AdiaComput
using QuSAT
using QuBase
using QuComputStates


    

In [31]:

    

function gap(aqc,t)
    H = Hamiltonian(aqc,t)|>coeffs|>full
    eigens = eigfact(H)[:values]|>real|>sort
    gap = eigens[2]-eigens[1]
    return gap
end

function findmin(n::Int;step=1e-2)
    ins,ans = generate(n)
    pH = QuArray(pHamiltonian(ins,n),(comput_basis(n),comput_basis(n)))

    aqc = AQC(pH,n;maxtime=1)

    t = 0
    pre = gap(aqc,0)
    eps = 100
    while eps>1e-2
        Δₜ = (gap(aqc,t)-gap(aqc,t+step))/step
        # if abs(Δₜ)>0.5
        #     Δₜ = sign(Δₜ)*0.5
        # end 
        
        # @show Δₜ
        t+=0.01*Δₜ
        # t = t - floor(t)
        cur = gap(aqc,t)
        # @show cur
        eps = pre - cur
        pre = cur
    end
    return t,gap(aqc,t)
end


    

    




WARNING: Method definition gap(Any, Any) in module Main at In[26]:2 overwritten at In[31]:2.
WARNING: Method definition findmin(Int64) in module Main at In[26]:9 overwritten at In[31]:9.
WARNING: Method definition #findmin(Array, Main.#findmin, Int64) in module Main overwritten.






    
Out[31]:





findmin (generic function with 1 method)

In [32]:

    

minimum_gap_distribution = Float64[]
minimum_gap = Float64[]

for i=4:9
    min = findmin(i)
    push!(minimum_gap_distribution,min[1])
    push!(minimum_gap,min[2])
end


    

In [41]:

    

using PyPlot

figure(figsize=(8,5))
bits = 4:9
normal = [0.07244126481043762,0.03735406347868613,0.019209135419509936,0.009850902273013015,0.005037811474275325,0.002569341455742216]
cooled = [0.2997385332640076,0.16215362712357842,0.09981409220957191,0.045814085969079335,0.02097595111357017,0.010066646959521903]
optimized_ratio = (cooled-normal)./normal

plot(bits,optimized_ratio,"o-",label="optimized ratio")
xlabel("qubits")
ylabel("optimized ratio")
ylim(2.8,4.3)

twinx()
ylabel("success probility")
plot(bits,normal,"go--",label="normal procedure")
plot(bits,cooled,"co-.",label="optimized")

xlim(3.5,9.5)
ylim(0.0,0.36)

legend()

savefig("")


    

    













    
Out[41]:





PyObject <matplotlib.legend.Legend object at 0x7f32fc37c110>

In [30]:

    

function ploteigen(n)
    ins,ans = generate(n)
    pH = QuArray(pHamiltonian(ins,n),(comput_basis(n),comput_basis(n)))
    aqc = AQC(pH,n;maxtime=1)
    
    return [gap(aqc,t) for t = 0.0:1e-2:1]
end


plot(ploteigen(6))


    

    













    




WARNING: Method definition ploteigen(Any) in module Main at In[24]:2 overwritten at In[30]:2.






    
Out[30]:





1-element Array{Any,1}:
 PyObject <matplotlib.lines.Line2D object at 0x7f32fc7b35d0>

In [80]:

    

figure(figsize=(10,5))
ax = gca()

xlabel("qubits")
ylabel("gap (J)")
p1, = ax[:plot](bits,minimum_gap,label="minimum gap","go-")
xlim(3.5,9.5)

ax2 = twinx()
ylabel("optimized ratio")
p2, = ax2[:plot](bits,optimized_ratio,label="optimized ratio","co--")
ylim(2.8,4.3)
xlim(3.5,9.5)

legend((p1,p2),("minimum gap","optimized ratio"))


    

    













    
Out[80]:





PyObject <matplotlib.legend.Legend object at 0x7f32fa6344d0>

In [86]:

    

figure(figsize=(10,5))
ax = gca()

xlabel("qubits")
ylabel("position")
p1, = ax[:plot](bits,abs(minimum_gap_distribution.-0.7),label="minimum gap position","go-")
xlim(3.5,9.5)

ax2 = twinx()
ylabel("optimized ratio")
p2, = ax2[:plot](bits,optimized_ratio,label="optimized ratio","co--")
ylim(2.8,4.3)
xlim(3.5,9.5)

legend((p1,p2),("relative position to 0.7","optimized ratio"))


    

    













    
Out[86]:





PyObject <matplotlib.legend.Legend object at 0x7f32fc08df10>

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